Media and entertainment services frequently recommend videos to end users that are selected to appeal to the users' interests. For example, videos are recommended based on predictions of a particular user's interest in particular videos that the user has not previously watched. Providing content that is more relevant to a user's interest can increase the user's viewing experience, and thereby increase the users' engagement with the recommend videos as well as related content, such as advertising content. Providing such recommendations has involved using historical data about how a set of many users has rated or consumed a set of videos. For example, based on recognizing that two users have both rated similar science fiction videos highly, videos that one of the users has not watched and the other user has rated highly can be recommended to the first user. More sophisticated techniques use ratings from many users in a set of users who have watched many of the videos in a large set of videos to provide video recommendations. In addition to using ratings, existing video recommendation techniques have used consumption data such as session progress data (e.g., identifying that a user watched the complete video or only a percentage of the video) to recommend videos that a user is mostly likely to fully consume.
Existing video recommendation techniques use collaborative filtering algorithms to analyze information about ratings or consumption to make video recommendations. For example, such techniques have used a user-by-video matrix with historical ratings for some, but not all, of the user/video points, and used matrix completion or matrix factorization techniques, e.g., singular value decomposition, k nearest neighbors, etc., to complete the matrix with predicted values. Recommendations are based on the predicted values that are determined. For example, for each user, videos that the user has not watched having the highest rating are recommended.
However, existing video recommendation techniques present certain disadvantages. For example, in certain cases, video recommendations may be provided that are not always appropriate or best-suited for a particular user. If a recommendation for a lengthy sports-related documentary is provided to a user while the user is at work at 10 am on a Monday morning, this recommendation ignores the fact that that user never watches documentaries or long programs while at work or between the hours of 8 and 5 on Mondays, and instead has historically watched short news-related video clips while at work between the hours of 8 and 5 on Mondays. In this example, or other cases where a recommendation is ill-suited to a user or the user's context, generating such recommendations utilize computing resources expended on the recommendation without enhancing a user's viewing experience or engagement with video content.